






import os
from collections.abc import Iterator





def 					snake_case	(  lowerCamelCase = "."       ):




	'''simple docstring'''







	for dir_path, dir_names, filenames in os.walk(lowerCamelCase       ):
		__lowercase   							=	[d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(lowerCamelCase       )[1] in (".py", ".ipynb"):
				yield os.path.join(lowerCamelCase					,	lowerCamelCase       ).lstrip("""./"""       )





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	return F'{i * "  "}*' if i else "\n##"





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	old_path.split(os.sep       )
	for i, new_part in enumerate(new_path.split(os.sep       )       ):
		if (i + 1 > len(lowerCamelCase       ) or old_parts[i] != new_part) and new_part:
			print(F'{md_prefix(lowerCamelCase       )} {new_part.replace("_"					,	" "       ).title()}'       )
	return new_path





def 					snake_case	(  lowerCamelCase = "."       ):




	'''simple docstring'''







	__lowercase   							=	""""""
	for filepath in sorted(good_file_paths(lowerCamelCase       )       ):
		__lowercase							,    __lowercase   							=	os.path.split(lowerCamelCase       )
		if filepath != old_path:
			__lowercase   							=	print_path(lowerCamelCase					,	lowerCamelCase       )
		__lowercase   							=	(filepath.count(os.sep       ) + 1) if filepath else 0
		__lowercase   							=	F'{filepath}/{filename}'.replace(""" """					,	"""%20"""       )
		__lowercase   							=	os.path.splitext(filename.replace("""_"""					,	""" """       ).title()       )[0]
		print(F'{md_prefix(lowerCamelCase       )} [{filename}]({url})'       )


if __name__ == "__main__":
						print_directory_md(""".""")









'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Dict  =					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"mvp"
					a_        =					["past_key_values"]
					a_        =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(  self  :		Any				, _A  :		List[str]=5_02_67				, _A  :		Dict=10_24				, _A  :		List[str]=12				, _A  :		Dict=40_96				, _A  :		Optional[int]=16				, _A  :		Optional[Any]=12				, _A  :		int=40_96				, _A  :		Dict=16				, _A  :		Union[str, Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		int="gelu"				, _A  :		Union[str, Any]=10_24				, _A  :		int=0.1				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.02				, _A  :		Optional[Any]=0.0				, _A  :		str=False				, _A  :		List[Any]=True				, _A  :		Optional[int]=1				, _A  :		Tuple=0				, _A  :		Dict=2				, _A  :		Dict=True				, _A  :		int=2				, _A  :		Tuple=2				, _A  :		List[str]=False				, _A  :		Any=1_00				, _A  :		Union[str, Any]=8_00				, **_A  :		List[str]				, )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       int          =       max_position_embeddings
								UpperCAmelCase_      :       Dict          =       d_model
								UpperCAmelCase_      :       Dict          =       encoder_ffn_dim
								UpperCAmelCase_      :       Optional[Any]          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       encoder_attention_heads
								UpperCAmelCase_      :       List[Any]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[Any]          =       decoder_layers
								UpperCAmelCase_      :       Dict          =       decoder_attention_heads
								UpperCAmelCase_      :       str          =       dropout
								UpperCAmelCase_      :       Optional[Any]          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       activation_function
								UpperCAmelCase_      :       Union[str, Any]          =       init_std
								UpperCAmelCase_      :       Optional[int]          =       encoder_layerdrop
								UpperCAmelCase_      :       Optional[int]          =       decoder_layerdrop
								UpperCAmelCase_      :       Dict          =       classifier_dropout
								UpperCAmelCase_      :       List[Any]          =       use_cache
								UpperCAmelCase_      :       Dict          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase_      :       int          =       use_prompt
								UpperCAmelCase_      :       Tuple          =       prompt_length
								UpperCAmelCase_      :       str          =       prompt_mid_dim

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, is_encoder_decoder=_A				, decoder_start_token_id=_A				, forced_eos_token_id=_A				, **_A				, )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				, _A      ):
											UpperCAmelCase_      :       Optional[Any]          =       self.bos_token_id
											warnings.warn(
											    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''      )


